This paper presents a method based on genetic algorithms and neural networks suitable for finding the five parameters of the Jiles-Atherton (JA) model for generalization to dynamic hysteresis loops. The aim is to obtain an equivalent static model for dynamic loops by updating its parameters varying the frequency of the imposed magnetic field H(t). Validations of the present approach compared to other numerical approaches, based on adding frequency-dependent losses to the static model, and versus experimental tests will be shown.

Salvini, A., RIGANTI FULGINEI, F. (2002). Genetic Algorithms and Neural Networks Generalizing the Jiles-Atherton Model of Static Hysteresis for Dynamic Loops. IEEE TRANSACTIONS ON MAGNETICS, Vol. 38, No. 2, 873-876 [10.1109/20.996225].

Genetic Algorithms and Neural Networks Generalizing the Jiles-Atherton Model of Static Hysteresis for Dynamic Loops

SALVINI, Alessandro;RIGANTI FULGINEI, Francesco
2002-01-01

Abstract

This paper presents a method based on genetic algorithms and neural networks suitable for finding the five parameters of the Jiles-Atherton (JA) model for generalization to dynamic hysteresis loops. The aim is to obtain an equivalent static model for dynamic loops by updating its parameters varying the frequency of the imposed magnetic field H(t). Validations of the present approach compared to other numerical approaches, based on adding frequency-dependent losses to the static model, and versus experimental tests will be shown.
2002
Salvini, A., RIGANTI FULGINEI, F. (2002). Genetic Algorithms and Neural Networks Generalizing the Jiles-Atherton Model of Static Hysteresis for Dynamic Loops. IEEE TRANSACTIONS ON MAGNETICS, Vol. 38, No. 2, 873-876 [10.1109/20.996225].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/159454
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